Study on a Robust Optimization Model for Project Scheduling Policies

2011 ◽  
Vol 110-116 ◽  
pp. 2866-2871
Author(s):  
Yu Zhang ◽  
Teng Fei Yin
Keyword(s):  
Genetic Algorithm ◽  
Robust Optimization ◽  
Objective Function ◽  
Project Scheduling ◽  
Optimization Model ◽  
Optimal Schedule ◽  
Optimization Method ◽  
Construction Process ◽  
Scheduling Policies ◽  
Project Environment

The genetic algorithm discussed in this paper for project scheduling solution to this problem can be obtained the near optimal schedule programs. This has established the objective function and constraints that have a certain scope; it requires the duration of each process that is determined in advance for enterprises. If the project is more familiar with the history with more experience, and more complete database, the project environment can be controlled well. It can accurately determine the time with the construction plan, construction process and the optimization method with the good trial.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

Logistics Network Optimization of Import Crude Oil in China Based on Genetic Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 3126-3129 ◽  
Author(s):  
Jie Chen
Keyword(s):  
Genetic Algorithm ◽  
Crude Oil ◽  
Objective Function ◽  
Network Optimization ◽  
Optimization Model ◽  
Transportation Network ◽  
Original Data ◽  
Coastal Areas ◽  
Logistics Network ◽  
Oil Transportation

The primary goal of this paper is to save logistics cost and reach optimizing configuration of import crude oil transportation network. An optimization model is put forward with an objective function of minimum logistics expense. It is carried out by Genetic Algorithm (GA) and MATLAB with original data of 2012 and predicted data of 2017. Results indicate that large VLCC of 260000-320000 tons is the main tanker type in import crude oil transportation network. And crude oil logistics bases will be formed which are represented by Qingdao, Dalian, Tianjin, Ningbo-Zhoushan, Zhanjiang and Huizhou in coastal areas.

scholarly journals Robust Optimization Model for Single Line Dynamic Bus Dispatching

2021 ◽  
Vol 14 (1) ◽  
pp. 73
Author(s):  
Yingxin Liu ◽  
Xinggang Luo ◽  
Xu Wei ◽  
Yang Yu ◽  
Jiafu Tang
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Arrival Rate ◽  
Optimization Method ◽  
Single Line ◽  
Passenger Flow ◽  
Bus Scheduling ◽  
Passenger Flows ◽  
The Stability ◽  
Prediction And Simulation

For effective bus operations, it is important to flexibly arrange the departure times of buses at the first station according to real-time passenger flows and traffic conditions. In dynamic bus dispatching research, existing optimization models are usually based on the prediction and simulation of passenger flow data. The bus departure schemes are formulated accordingly, and the passenger arrival rate uncertainty must be considered. Robust optimization is a common and effective method to handle such uncertainty problems. This paper introduces a robust optimization method for single-line dynamic bus scheduling. By setting three scenarios—the benchmark passenger flow, high passenger flow, and low passenger flow—the robust optimization model of dynamic bus departures is established with consideration of different passenger arrival rates in different scenarios. A genetic algorithm (GA) is improved for minimizing the total passenger waiting time. The results obtained by the proposed optimization method are compared with those from a stochastic programming method. The standard deviation of the relative regret value with stochastic optimization is 5.42%, whereas that of the relative regret value with robust optimization is 0.62%. The stability of robust optimization is better, and the fluctuation degree is greatly reduced.

Optimization Method for Robust Continuous Parameter Design in the Target-being-Best Based on Genetic Evolution

2014 ◽  
Vol 1049-1050 ◽  
pp. 1272-1280
Author(s):  
Qiang Zeng ◽  
Ling Shen ◽  
Ze Bin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Theoretical Calculation ◽  
Optimization Model ◽  
Optimization Method ◽  
Parameter Design ◽  
Genetic Evolution ◽  
Quality Loss ◽  
Average Quality ◽  
Continuous Parameter

Aiming at the problem of robust continuous parameter design in the Target-being-best, in which the output value can be obtained by theoretical calculation, an optimization method based on genetic evolution is proposed. Firstly, the researched problem is described mathematically and an optimization model is established with the objective to minimize the average quality loss of a sample. Secondly, the optimization method based on genetic evolution for the researched problem is proposed. Thirdly, the genetic algorithm for robust continuous parameter design in the Target-being-best is presented and designed. Finally, the effectiveness of the proposed method is validated by case study.

scholarly journals Optimization Method of Locomotive Working Diagram Layout

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yajing Zheng ◽  
Wenzhou Jin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimization Method ◽  
The Optimizing Model ◽  
Optimizing Model

Rational scheduling of locomotive paths (the locus of the locomotive point in the train working diagram) is an important step in drawing a locomotive working diagram by a computer. But there are some problems in this process, such as the computer usually drawing a locomotive path that overlaps with another locomotive path (in the circumstances, the actual users of the locomotive working diagrams often misread the locomotive planning). At present, there are many studies about assigning sets of locomotives to each train in a preplanned train schedule; in contrast, the studies of visualizing the locomotive planning are relatively rare. Through investigating the locomotive working diagram users, this paper points out that the layout of locomotive paths should put the distance between lines being as large as possible and should put the number of the intersection between lines being as few as possible as the optimization aim which is based to solve the problem of the lines overlap or the problem of the lines beyond the margins for drawing the locomotive paths. This paper also builds the optimization model of locomotive working diagram layout. Based on determining the position of locomotive paths which can be delineated, a genetic algorithm is used to solve the optimizing model of locomotive working diagram layout in this paper. An example of a train working diagram with 36 trains is given at the end of the paper, which indicates that the optimization model of locomotive working diagram layout can better solve the problem of locomotive planning visualization.

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